Principal Components in the Nonnormal Case: The Test for Sphericity.

Abstract

The limiting distribution of the likelihood ratio statistic W sub q for testing the hypothesis of equality of q characteristic roots of a covariance matrix for normal populations is studied for nonnormal populations. It is shown, both theoretically and empirically, that the limiting distribution of W sub q is not robust to departures from normality characterized by nonzero fourth cumulants and that W sub q cannot be used for these nonnormal populations. For the class of spherically symmetric populations, it is shown that the limiting distribution of W sub q is proportional to a chi-square under the null hypothesis of equality of q population roots and to a noncentral chi-square under an appropriate sequence of alternative hypotheses. A corrected test statistic, W sub q, whose limiting distribution is chi-square, is proposed to test the hypothesis of sphericity for the general class of spherically symmetric populations. Results of a Monte Carlo experiment conducted to compare the performances of W sub q and W sub q are presented for various contaminated normal models at various sample sizes. It is demonstrated that there is little difference between W sub q and W sub q for normal populations, but that dramatic improvements are gained by using W sub q for the nonnormal populations. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1977
Accession Number
ADA049622

Entities

People

  • Christine M. Waternaux

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Series
  • Confidence Limits
  • Covariance
  • Data Analysis
  • Data Mining
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Maximum Likelihood Estimation
  • Military Research
  • Probability
  • Random Variables
  • Sampling
  • Sequences
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.